Preference-based measures of health-related quality of life in Indigenous people: a systematic review
Bibliographic record
Abstract
PURPOSE: In many countries, there are calls to address health inequalities experienced by Indigenous people. Preference-based measures (PBMs) provide a measurement of health-related quality of life and can support resource allocation decisions. This review aimed to identify, summarize, and appraise the literature reporting the use and performance of PBMs with Indigenous people. METHODS: Eleven major databases were searched from inception to August 31, 2022. Records in English that (1) assessed any measurement property of PBMs, (2) directly elicited health preferences, (3) reported the development or translation of PBMs for Indigenous people, or (4) measured health-related quality of life (HRQL) using PBMs were included. Ethically engaged research with Indigenous people was considered as an element of methodological quality. Data was synthesized descriptively (PROSPERO ID: CRD42020205239). RESULTS: Of 3139 records identified, 81 were eligible, describing psychometric evaluation (n = 4), preference elicitation (n = 4), development (n = 4), translation (n = 2), and HRQL measurement (n = 71). 31 reported ethically engaged research. Reports originated primarily from Australia (n = 38), New Zealand (n = 20), USA (n = 9) and Canada (n = 6). Nearly all (n = 73) reported indirect, multi-attribute PBMs, the most common of which was the EQ-5D (n = 50). CONCLUSION: A large number of recent publications from diverse disciplines report the use of PBMs with Indigenous people, despite little evidence on measurement properties in these populations. Understanding the measurement properties of PBMs with Indigenous people is important to better understand how these measures might, or might not, be used in policy and resource decisions affecting Indigenous people. (Funding: EuroQoL Research Foundation).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".